The present invention relates generally to artificial intelligence system and particularly to the field of digital computer-aided knowledge acquisition and reasoning system. The system developed is called the Relational Artificial Intelligence System (RAIS). RAIS comprises a relational automatic knowledge acquisition system and a relational reasoning system. The relational automatic knowledge acquisition system is a relational learning system (RLS) and the relational reasoning system is a relational knowledge-based system (RKBS). Therefore the background of both learning system and knowledge-based system needs to be discussed here.
The knowledge-based system (KBS) also called the expert system is one of the most successful branches in Artificial Intelligence (AI), and has been successfully applied in different areas, such as engineering, business, medicine, etc. Currently, most KBSs are created by using expert system shells, and this simplifies the process of building expert systems. In most of current expert system shells, the inference engine is built-in, so the creation of an expert system in any domain is simplified to the creation of a knowledge base in the specific domain.
The built-in inference engine is a computer program. It can reason about the knowledge base in the required format. There are three most frequently used types of knowledge bases: rule-based, frame-based, and predicate expressions.
Among these three types, the rule-based knowledge base is the most commonly used. Rule-bases are built by the cooperation of domain experts and knowledge engineers who are software engineers familiar with the structures and requirements of expert systems and expert system shells. Generally speaking, the "if-then" rules or frames of knowledge bases are totally different from any knowledge representation formats in domain experts' professional or daily life. Domain experts need the help of knowledge engineers in the process of design and creation of any knowledge bases. Before being executed, the created knowledge base needs to be compiled and integrated with the inference engine.
The cooperation of domain experts and knowledge engineers is the most time-consuming, and maybe the most cost-consuming process. And it is the most difficult part in building expert systems. Since 70's, it is believed that the process, which involves domain experts and knowledge engineers working together to design, construct, and modify the domain knowledge base, is the main bottleneck in the development of expert systems.
There are two different ways to solve the above-mentioned bottleneck problem:
The first one is to make the system acquire knowledge automatically by itself. To acquire knowledge from data readings or databases directly is one of the most important research areas. Most current databases are relational or object-oriented, i.e., they are spreadsheet-formed, but most current knowledge bases are rule-based or frame-based. It's no easy task taking spreadsheet-formed databases as sources, and rule-formed or frame-formed knowledge bases as output targets at the same time through the learning system. Up to now, no big success in this area has been achieved.
The second one is to make the KBS user-friendly such that domain experts can input domain knowledge by themselves. Some authors developed spreadsheet-formed interfaces between the user and the rule-based KBS. However, because their knowledge bases are still rule-based, such systems are either designed for specific domains or not homogeneously integrated. Moreover, compilation and integration for such systems are still necessary after knowledge is inputted or modified.